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Report NEP-BIG-2021-04-19
This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-BIG
The following items were announced in this report:
- Benetos, Emmanouil & Ragano, Alessandro & Sgroi, Daniel & Tuckwell, Anthony, 2021, "Measuring National Life Satisfaction with Music," IZA Discussion Papers, Institute of Labor Economics (IZA), number 14258, Apr.
- Kai A. Konrad, 2019, "Attacking and Defending Multiple Valuable Secrets in a Big Data World," Working Papers, Max Planck Institute for Tax Law and Public Finance, number tax-mpg-rps-2019-05, May.
- Jia Wang & Tong Sun & Benyuan Liu & Yu Cao & Degang Wang, 2021, "Financial Markets Prediction with Deep Learning," Papers, arXiv.org, number 2104.05413, Apr.
- Przemys{l}aw Biecek & Marcin Chlebus & Janusz Gajda & Alicja Gosiewska & Anna Kozak & Dominik Ogonowski & Jakub Sztachelski & Piotr Wojewnik, 2021, "Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models," Papers, arXiv.org, number 2104.06735, Apr.
- Ling Qi & Matloob Khushi & Josiah Poon, 2021, "Event-Driven LSTM For Forex Price Prediction," Papers, arXiv.org, number 2102.01499, Jan.
- Seema Jayachandran & Monica Biradavolu & Jan Cooper, 2021, "Using Machine Learning and Qualitative Interviews to Design a Five-Question Women's Agency Index," CESifo Working Paper Series, CESifo, number 8984.
- Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020, "Local mortality estimates during the COVID-19 pandemic in Italy," Discussion Paper series in Regional Science & Economic Geography, Gran Sasso Science Institute, Social Sciences, number 2020-06, Oct, revised Oct 2020.
- Andreas Joseph & Eleni Kalamara & George Kapetanios & Galina Potjagailo & Chiranjit Chakraborty, 2021, "Forecasting UK inflation bottom up," Bank of England working papers, Bank of England, number 915, Mar.
- Nazish Ashfaq & Zubair Nawaz & Muhammad Ilyas, 2021, "A comparative study of Different Machine Learning Regressors For Stock Market Prediction," Papers, arXiv.org, number 2104.07469, Apr.
- Daniel Straulino & Juan C. Saldarriaga & Jairo A. G'omez & Juan C. Duque & Neave O'Clery, 2021, "Uncovering commercial activity in informal cities," Papers, arXiv.org, number 2104.04545, Apr.
- Jean Jacques Ohana & Eric Benhamou & David Saltiel & Beatrice Guez, 2021, "Is the Covid equity bubble rational? A machine learning answer," Working Papers, HAL, number hal-03189799, Apr.
- Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021, "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print, HAL, number hal-03184841.
- Denuit, Michel & Charpentier, Arthur & Trufin, Julien, 2021, "Autocalibration and Tweedie-dominance for insurance pricing with machine learning," LIDAM Discussion Papers ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), number 2021013, Mar.
- Item repec:baf:cbafwp:cbafwp20160 is not listed on IDEAS anymore
- Jia Wang & Tong Sun & Benyuan Liu & Yu Cao & Hongwei Zhu, 2021, "CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets," Papers, arXiv.org, number 2104.04041, Apr.
- Marianne Bertrand & Bruno Crépon & Alicia Marguerie & Patrick Premand, 2021, "Do Workfare Programs Live Up to Their Promises? Experimental Evidence from Cote D’Ivoire," NBER Working Papers, National Bureau of Economic Research, Inc, number 28664, Apr.
- Margherita Pagani & Renaud Champion, 2020, "Making Sense of the AI Landscape," Post-Print, HAL, number hal-03188248, Nov.
- Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021, "Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model," Papers, arXiv.org, number 2104.06259, Apr.
- Ekaterina Prytkova, 2021, "ICT's Wide Web: a System-Level Analysis of ICT's Industrial Diffusion with Algorithmic Links," Jena Economics Research Papers, Friedrich-Schiller-University Jena, number 2021-005, Mar.
- Fabrizio Lillo & Giulia Livieri & Stefano Marmi & Anton Solomko & Sandro Vaienti, 2021, "Analysis of bank leverage via dynamical systems and deep neural networks," Papers, arXiv.org, number 2104.04960, Apr.
- Trufin, Julien & Denuit, Michel, 2021, "Boosting cost-complexity pruned trees On Tweedie responses: the ABT machine," LIDAM Discussion Papers ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), number 2021015, Mar.
- Daniel Boller & Michael Lechner & Gabriel Okasa, 2021, "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers, arXiv.org, number 2104.04601, Apr.
- Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021, "Black-box model risk in finance," Papers, arXiv.org, number 2102.04757, Feb.
- Marco Due~nas & Federico Nutarelli & V'ictor Ortiz & Massimo Riccaboni & Francesco Serti, 2021, "Assessing the Heterogeneous Impact of Economy-Wide Shocks: A Machine Learning Approach Applied to Colombian Firms," Papers, arXiv.org, number 2104.04570, Apr, revised Nov 2024.
- Hainaut, Donatien & Trufin, Julien & Denuit, Michel, 2021, "Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link," LIDAM Discussion Papers ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), number 2021012, Jan.